# Monte Carlo sampling variant of the DSSV14 set of helicity parton densities

## TL;DR

This paper introduces a Monte Carlo sampling method to extract helicity parton densities and their uncertainties from polarized scattering data, providing a flexible and statistically robust alternative to traditional fitting approaches.

## Contribution

It presents a novel Monte Carlo sampling strategy for helicity parton densities, allowing flexible parametrizations and improved uncertainty estimation compared to prior methods.

## Key findings

- Good agreement with traditional fitting and Lagrange multiplier methods.
- Demonstrates impact of recent STAR dijet data on gluon helicity density.
- Provides a new set of replicas for further analyses.

## Abstract

We implement a Monte Carlo sampling strategy to extract helicity parton densities and their uncertainties from a reference set of longitudinally polarized scattering data, chosen to be that used in the DSSV14 global analysis. Instead of adopting the simplest possible functional forms for the helicity parton distributions and imposing certain restrictions on their parameter space in order to constrain them, we employ redundant, flexible parametrizations and fit them to a large number of Monte Carlo replicas of the existing data. The optimum fit and its uncertainty estimates are then assumed to be given by the statistical average of the obtained ensemble of replicas of helicity parton densities and their corresponding variance, respectively. We compare our results to those obtained by the traditional fitting approach and to the uncertainty estimates derived with the robust Lagrange multiplier method, finding good agreement. As a first application of our new set of replicas, we discuss the impact of the recent STAR dijet data in further constraining the elusive gluon helicity density through the reweighting method.